Preferential Attachment Random Graphs with Edge-Step Functions

Caio Alves (), Rodrigo Ribeiro () and Rémy Sanchis ()
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Caio Alves: University of Leipzig
Rodrigo Ribeiro: IMPA, Estrada Da. Castorina
Rémy Sanchis: Universidade Federal de Minas Gerais

Journal of Theoretical Probability, 2021, vol. 34, issue 1, 438-476

Abstract: Abstract We analyze a random graph model with preferential attachment rule and edge-step functions that govern the growth rate of the vertex set, and study the effect of these functions on the empirical degree distribution of these random graphs. More specifically, we prove that when the edge-step function f is a monotone regularly varying function at infinity, the degree sequence of graphs associated with it obeys a (generalized) power-law distribution whose exponent belongs to (1, 2] and is related to the index of regular variation of f at infinity whenever said index is greater than $$-1$$ - 1 . When the regular variation index is less than or equal to $$-1$$ - 1 , we show that the empirical degree distribution vanishes for any fixed degree.

Keywords: Complex networks; Preferential attachment; Concentration bounds; Power-law; Scale-free; Karamata’s theory; Regularly varying functions; Primary 05C82; Secondary 60K40; 68R10 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10959-019-00959-0

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